Maximum a posteriori state estimation: a neural processing algorithm

A computational algorithm is presented for obtaining the maximum a posteriori estimates of the states of a stochastic dynamical system by programming a neural network. It is well known that for real-time control implementations, especially in such applications as multitarget tracking and vision-guided robots, the computational requirements for solving such state estimation problems attain particular significance, and parallel processing techniques are highly useful. The performance of the algorithm has been investigated by conducting several numerical experiments. It appears to be useful for handling state estimation problems arising in real-world applications